Overview

Dataset statistics

Number of variables18
Number of observations10649317
Missing cells13495801
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 GiB
Average record size in memory144.0 B

Variable types

Numeric7
Categorical11

Alerts

start_time has a high cardinality: 8706 distinct values High cardinality
blueprint has a high cardinality: 441 distinct values High cardinality
login has a high cardinality: 1414 distinct values High cardinality
position_x is highly correlated with map_width and 1 other fieldsHigh correlation
position_y is highly correlated with map_width and 1 other fieldsHigh correlation
map_width is highly correlated with position_x and 2 other fieldsHigh correlation
map_height is highly correlated with position_x and 2 other fieldsHigh correlation
position_x is highly correlated with map_width and 1 other fieldsHigh correlation
position_y is highly correlated with map_width and 1 other fieldsHigh correlation
map_width is highly correlated with position_x and 2 other fieldsHigh correlation
map_height is highly correlated with position_x and 2 other fieldsHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
map_name is highly correlated with map_height and 2 other fieldsHigh correlation
map_height is highly correlated with map_name and 2 other fieldsHigh correlation
map_width is highly correlated with map_name and 2 other fieldsHigh correlation
map_thumbnail is highly correlated with map_name and 2 other fieldsHigh correlation
position_x is highly correlated with position_y and 4 other fieldsHigh correlation
position_y is highly correlated with position_x and 4 other fieldsHigh correlation
faf_player_id is highly correlated with ratingHigh correlation
rating is highly correlated with faf_player_id and 2 other fieldsHigh correlation
map_name is highly correlated with position_x and 5 other fieldsHigh correlation
map_thumbnail is highly correlated with position_x and 5 other fieldsHigh correlation
map_width is highly correlated with position_x and 4 other fieldsHigh correlation
map_height is highly correlated with position_x and 4 other fieldsHigh correlation
blueprint has 7697205 (72.3%) missing values Missing
position_x has 2899298 (27.2%) missing values Missing
position_y has 2899298 (27.2%) missing values Missing
units_number has 264488 (2.5%) zeros Zeros
rating has 115913 (1.1%) zeros Zeros

Reproduction

Analysis started2022-07-28 03:07:39.823401
Analysis finished2022-07-28 03:19:23.622767
Duration11 minutes and 43.8 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

Distinct8860
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16572264.96
Minimum16462952
Maximum16683253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.2 MiB
2022-07-27T23:19:23.678265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16462952
5-th percentile16473326
Q116517949
median16570283
Q316627127
95-th percentile16672501
Maximum16683253
Range220301
Interquartile range (IQR)109178

Descriptive statistics

Standard deviation63479.47416
Coefficient of variation (CV)0.003830464593
Kurtosis-1.187056363
Mean16572264.96
Median Absolute Deviation (MAD)54673
Skewness0.04073272443
Sum1.76483303 × 1014
Variance4029643639
MonotonicityNot monotonic
2022-07-27T23:19:23.739517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166136288336
 
0.1%
165770018179
 
0.1%
165594438156
 
0.1%
166518327853
 
0.1%
165112107391
 
0.1%
166290457305
 
0.1%
165481067282
 
0.1%
166619167054
 
0.1%
166031656942
 
0.1%
166443876705
 
0.1%
Other values (8850)10574114
99.3%
ValueCountFrequency (%)
16462952973
 
< 0.1%
164629912473
< 0.1%
16463034449
 
< 0.1%
164632021005
< 0.1%
164632132075
< 0.1%
164632551474
< 0.1%
164632561255
< 0.1%
164632881608
< 0.1%
164634171585
< 0.1%
16463462986
 
< 0.1%
ValueCountFrequency (%)
166832536583
0.1%
16683220476
 
< 0.1%
166831691443
 
< 0.1%
16683156898
 
< 0.1%
16683099391
 
< 0.1%
16683084236
 
< 0.1%
166830633469
< 0.1%
166830622377
 
< 0.1%
166830332435
 
< 0.1%
166830281288
 
< 0.1%

start_time
Categorical

HIGH CARDINALITY

Distinct8706
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
2022-03-17 20:34:03 UTC
 
10518
2022-03-22 18:46:46 UTC
 
8336
2022-03-14 22:42:58 UTC
 
8156
2022-03-16 22:34:05 UTC
 
7884
2022-03-27 16:46:40 UTC
 
7853
Other values (8701)
10606570 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters244934291
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)< 0.1%

Sample

1st row2022-03-25 09:22:13 UTC
2nd row2022-03-11 20:19:06 UTC
3rd row2022-03-14 00:27:49 UTC
4th row2022-03-14 00:27:49 UTC
5th row2022-03-27 14:52:40 UTC

Common Values

ValueCountFrequency (%)
2022-03-17 20:34:03 UTC10518
 
0.1%
2022-03-22 18:46:46 UTC8336
 
0.1%
2022-03-14 22:42:58 UTC8156
 
0.1%
2022-03-16 22:34:05 UTC7884
 
0.1%
2022-03-27 16:46:40 UTC7853
 
0.1%
2022-03-08 04:19:04 UTC7391
 
0.1%
2022-03-25 00:37:10 UTC7305
 
0.1%
2022-03-13 12:37:18 UTC7282
 
0.1%
2022-03-28 21:46:49 UTC7054
 
0.1%
2022-03-21 03:28:33 UTC6942
 
0.1%
Other values (8696)10570596
99.3%

Length

2022-07-27T23:19:23.796425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc10649317
33.3%
2022-03-13462737
 
1.4%
2022-03-12457122
 
1.4%
2022-03-25415463
 
1.3%
2022-03-27412049
 
1.3%
2022-03-07408362
 
1.3%
2022-03-06395657
 
1.2%
2022-03-19394976
 
1.2%
2022-03-28380870
 
1.2%
2022-03-24366422
 
1.1%
Other values (7657)17604976
55.1%

Most occurring characters

ValueCountFrequency (%)
244607209
18.2%
035710181
14.6%
-21298634
8.7%
21298634
8.7%
:21298634
8.7%
318321225
7.5%
118312947
7.5%
U10649317
 
4.3%
T10649317
 
4.3%
C10649317
 
4.3%
Other values (6)32138876
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number149090438
60.9%
Uppercase Letter31947951
 
13.0%
Dash Punctuation21298634
 
8.7%
Space Separator21298634
 
8.7%
Other Punctuation21298634
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
244607209
29.9%
035710181
24.0%
318321225
12.3%
118312947
12.3%
47874342
 
5.3%
57601750
 
5.1%
94314105
 
2.9%
74150981
 
2.8%
84110005
 
2.8%
64087693
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
U10649317
33.3%
T10649317
33.3%
C10649317
33.3%
Dash Punctuation
ValueCountFrequency (%)
-21298634
100.0%
Space Separator
ValueCountFrequency (%)
21298634
100.0%
Other Punctuation
ValueCountFrequency (%)
:21298634
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212986340
87.0%
Latin31947951
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
244607209
20.9%
035710181
16.8%
-21298634
10.0%
21298634
10.0%
:21298634
10.0%
318321225
8.6%
118312947
8.6%
47874342
 
3.7%
57601750
 
3.6%
94314105
 
2.0%
Other values (3)12348679
 
5.8%
Latin
ValueCountFrequency (%)
U10649317
33.3%
T10649317
33.3%
C10649317
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII244934291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244607209
18.2%
035710181
14.6%
-21298634
8.7%
21298634
8.7%
:21298634
8.7%
318321225
7.5%
118312947
7.5%
U10649317
 
4.3%
T10649317
 
4.3%
C10649317
 
4.3%
Other values (6)32138876
13.1%

offset_ms
Real number (ℝ≥0)

Distinct59864
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793221.0723
Minimum0
Maximum8623200
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size81.2 MiB
2022-07-27T23:19:23.852496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile73900
Q1311800
median604300
Q31052200
95-th percentile2179000
Maximum8623200
Range8623200
Interquartile range (IQR)740400

Descriptive statistics

Standard deviation707399.6088
Coefficient of variation (CV)0.8918063747
Kurtosis7.652590491
Mean793221.0723
Median Absolute Deviation (MAD)342400
Skewness2.135964824
Sum8.44726265 × 1012
Variance5.004142065 × 1011
MonotonicityNot monotonic
2022-07-27T23:19:23.912185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3175001076
 
< 0.1%
878001075
 
< 0.1%
3249001057
 
< 0.1%
3606001055
 
< 0.1%
3168001054
 
< 0.1%
875001050
 
< 0.1%
873001041
 
< 0.1%
3183001040
 
< 0.1%
3130001037
 
< 0.1%
874001037
 
< 0.1%
Other values (59854)10638795
99.9%
ValueCountFrequency (%)
02
 
< 0.1%
18001
 
< 0.1%
19001
 
< 0.1%
2000101
< 0.1%
210011
 
< 0.1%
22009
 
< 0.1%
230015
 
< 0.1%
240017
 
< 0.1%
250016
 
< 0.1%
260026
 
< 0.1%
ValueCountFrequency (%)
86232001
< 0.1%
86181001
< 0.1%
86066001
< 0.1%
86008001
< 0.1%
86005001
< 0.1%
85996001
< 0.1%
85990001
< 0.1%
85986001
< 0.1%
85975001
< 0.1%
85928001
< 0.1%

player
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
1
5358541 
0
5290776 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10649317
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

Length

2022-07-27T23:19:23.965201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:19:24.012538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

Most occurring characters

ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10649317
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common10649317
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10649317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15358541
50.3%
05290776
49.7%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
issue
10276905 
factory_issue
 
372412

Length

Max length13
Median length5
Mean length5.279764045
Min length5

Characters and Unicode

Total characters56225881
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowissue
2nd rowissue
3rd rowissue
4th rowissue
5th rowissue

Common Values

ValueCountFrequency (%)
issue10276905
96.5%
factory_issue372412
 
3.5%

Length

2022-07-27T23:19:24.054974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:19:24.104488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
issue10276905
96.5%
factory_issue372412
 
3.5%

Most occurring characters

ValueCountFrequency (%)
s21298634
37.9%
i10649317
18.9%
u10649317
18.9%
e10649317
18.9%
f372412
 
0.7%
a372412
 
0.7%
c372412
 
0.7%
t372412
 
0.7%
o372412
 
0.7%
r372412
 
0.7%
Other values (2)744824
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter55853469
99.3%
Connector Punctuation372412
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s21298634
38.1%
i10649317
19.1%
u10649317
19.1%
e10649317
19.1%
f372412
 
0.7%
a372412
 
0.7%
c372412
 
0.7%
t372412
 
0.7%
o372412
 
0.7%
r372412
 
0.7%
Connector Punctuation
ValueCountFrequency (%)
_372412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55853469
99.3%
Common372412
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s21298634
38.1%
i10649317
19.1%
u10649317
19.1%
e10649317
19.1%
f372412
 
0.7%
a372412
 
0.7%
c372412
 
0.7%
t372412
 
0.7%
o372412
 
0.7%
r372412
 
0.7%
Common
ValueCountFrequency (%)
_372412
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII56225881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s21298634
37.9%
i10649317
18.9%
u10649317
18.9%
e10649317
18.9%
f372412
 
0.7%
a372412
 
0.7%
c372412
 
0.7%
t372412
 
0.7%
o372412
 
0.7%
r372412
 
0.7%
Other values (2)744824
 
1.3%

units_number
Real number (ℝ≥0)

ZEROS

Distinct498
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.795839583
Minimum0
Maximum1158
Zeros264488
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size81.2 MiB
2022-07-27T23:19:24.149159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q36
95-th percentile32
Maximum1158
Range1158
Interquartile range (IQR)5

Descriptive statistics

Standard deviation15.11830828
Coefficient of variation (CV)2.224641724
Kurtosis187.8420486
Mean6.795839583
Median Absolute Deviation (MAD)0
Skewness7.802602789
Sum72371050
Variance228.5632454
MonotonicityNot monotonic
2022-07-27T23:19:24.207456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15841480
54.9%
2706871
 
6.6%
3474870
 
4.5%
4374277
 
3.5%
5299445
 
2.8%
0264488
 
2.5%
6243910
 
2.3%
7200120
 
1.9%
8174235
 
1.6%
9149773
 
1.4%
Other values (488)1919848
 
18.0%
ValueCountFrequency (%)
0264488
 
2.5%
15841480
54.9%
2706871
 
6.6%
3474870
 
4.5%
4374277
 
3.5%
5299445
 
2.8%
6243910
 
2.3%
7200120
 
1.9%
8174235
 
1.6%
9149773
 
1.4%
ValueCountFrequency (%)
11581
< 0.1%
10301
< 0.1%
10251
< 0.1%
10242
< 0.1%
10131
< 0.1%
10081
< 0.1%
9981
< 0.1%
9761
< 0.1%
9661
< 0.1%
9581
< 0.1%

blueprint
Categorical

HIGH CARDINALITY
MISSING

Distinct441
Distinct (%)< 0.1%
Missing7697205
Missing (%)72.3%
Memory size81.2 MiB
ueb1103
 
220610
urb1103
 
166992
url0107
 
103988
uel0201
 
103376
xsb1103
 
92688
Other values (436)
2264458 

Length

Max length22
Median length7
Mean length7.003478527
Min length7

Characters and Unicode

Total characters20675053
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowueb1103
2nd rowurb0102
3rd rowueb1103
4th rowueb1202
5th rowurb1101

Common Values

ValueCountFrequency (%)
ueb1103220610
 
2.1%
urb1103166992
 
1.6%
url0107103988
 
1.0%
uel0201103376
 
1.0%
xsb110392688
 
0.9%
uel010588480
 
0.8%
ueb010179580
 
0.7%
url010570815
 
0.7%
uab110369880
 
0.7%
ueb110168876
 
0.6%
Other values (431)1886827
 
17.7%
(Missing)7697205
72.3%

Length

2022-07-27T23:19:24.265583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ueb1103220610
 
7.5%
urb1103166992
 
5.7%
url0107103988
 
3.5%
uel0201103376
 
3.5%
xsb110392688
 
3.1%
uel010588480
 
3.0%
ueb010179580
 
2.7%
url010570815
 
2.4%
uab110369880
 
2.4%
ueb110168876
 
2.3%
Other values (431)1886827
63.9%

Most occurring characters

ValueCountFrequency (%)
04495056
21.7%
14079725
19.7%
u2386985
11.5%
b1729385
 
8.4%
21280276
 
6.2%
e1194822
 
5.8%
31051480
 
5.1%
l977823
 
4.7%
r928906
 
4.5%
a587924
 
2.8%
Other values (23)1962671
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11803363
57.1%
Lowercase Letter8869675
42.9%
Connector Punctuation2015
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u2386985
26.9%
b1729385
19.5%
e1194822
13.5%
l977823
11.0%
r928906
 
10.5%
a587924
 
6.6%
x498342
 
5.6%
s489046
 
5.5%
z34295
 
0.4%
d32585
 
0.4%
Other values (12)9562
 
0.1%
Decimal Number
ValueCountFrequency (%)
04495056
38.1%
14079725
34.6%
21280276
 
10.8%
31051480
 
8.9%
5374115
 
3.2%
4237862
 
2.0%
7117171
 
1.0%
673256
 
0.6%
949397
 
0.4%
845025
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_2015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11805378
57.1%
Latin8869675
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
u2386985
26.9%
b1729385
19.5%
e1194822
13.5%
l977823
11.0%
r928906
 
10.5%
a587924
 
6.6%
x498342
 
5.6%
s489046
 
5.5%
z34295
 
0.4%
d32585
 
0.4%
Other values (12)9562
 
0.1%
Common
ValueCountFrequency (%)
04495056
38.1%
14079725
34.6%
21280276
 
10.8%
31051480
 
8.9%
5374115
 
3.2%
4237862
 
2.0%
7117171
 
1.0%
673256
 
0.6%
949397
 
0.4%
845025
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII20675053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04495056
21.7%
14079725
19.7%
u2386985
11.5%
b1729385
 
8.4%
21280276
 
6.2%
e1194822
 
5.8%
31051480
 
5.1%
l977823
 
4.7%
r928906
 
4.5%
a587924
 
2.8%
Other values (23)1962671
9.5%

position_x
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5108902
Distinct (%)65.9%
Missing2899298
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean260.7909918
Minimum-0.9940414429
Maximum1023.993225
Zeros2
Zeros (%)< 0.1%
Negative146
Negative (%)< 0.1%
Memory size81.2 MiB
2022-07-27T23:19:24.354374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9940414429
5-th percentile51.17489624
Q1134.0476532
median234.9521484
Q3361.5
95-th percentile552.5
Maximum1023.993225
Range1024.987267
Interquartile range (IQR)227.4523468

Descriptive statistics

Standard deviation163.8237293
Coefficient of variation (CV)0.6281801689
Kurtosis1.466136818
Mean260.7909918
Median Absolute Deviation (MAD)110.216095
Skewness1.052628312
Sum2021135141
Variance26838.21427
MonotonicityNot monotonic
2022-07-27T23:19:24.681544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246.510201
 
0.1%
108.510086
 
0.1%
72.59623
 
0.1%
128.59621
 
0.1%
439.59502
 
0.1%
446.59499
 
0.1%
264.59280
 
0.1%
261.59152
 
0.1%
188.58536
 
0.1%
75.58431
 
0.1%
Other values (5108892)7656088
71.9%
(Missing)2899298
 
27.2%
ValueCountFrequency (%)
-0.99404144291
< 0.1%
-0.99298858641
< 0.1%
-0.99010467531
< 0.1%
-0.9863281251
< 0.1%
-0.98284912111
< 0.1%
-0.97949218751
< 0.1%
-0.97653961181
< 0.1%
-0.96988677981
< 0.1%
-0.96672058111
< 0.1%
-0.9558715821
< 0.1%
ValueCountFrequency (%)
1023.9932251
< 0.1%
1023.5510251
< 0.1%
1023.4320071
< 0.1%
1023.3285521
< 0.1%
1023.2798461
< 0.1%
1023.2647711
< 0.1%
1023.2498781
< 0.1%
1023.0720831
< 0.1%
1022.8950811
< 0.1%
1022.7973631
< 0.1%

position_y
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5030610
Distinct (%)64.9%
Missing2899298
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean266.1674053
Minimum-0.9997253418
Maximum1023.999512
Zeros2
Zeros (%)< 0.1%
Negative63
Negative (%)< 0.1%
Memory size81.2 MiB
2022-07-27T23:19:24.773301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9997253418
5-th percentile50.10795975
Q1136.6473618
median236.6968079
Q3371.4170532
95-th percentile572.1413269
Maximum1023.999512
Range1024.999237
Interquartile range (IQR)234.7696915

Descriptive statistics

Standard deviation167.4089029
Coefficient of variation (CV)0.6289609456
Kurtosis1.178209145
Mean266.1674053
Median Absolute Deviation (MAD)116.2043991
Skewness0.9770891561
Sum2062802448
Variance28025.74078
MonotonicityNot monotonic
2022-07-27T23:19:24.833578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222.511466
 
0.1%
70.59588
 
0.1%
421.59224
 
0.1%
400.59145
 
0.1%
62.58899
 
0.1%
88.58698
 
0.1%
449.58574
 
0.1%
357.58092
 
0.1%
45.58007
 
0.1%
142.57889
 
0.1%
Other values (5030600)7660437
71.9%
(Missing)2899298
 
27.2%
ValueCountFrequency (%)
-0.99972534181
 
< 0.1%
-0.97908020021
 
< 0.1%
-0.95717620851
 
< 0.1%
-0.95675659181
 
< 0.1%
-0.95567321781
 
< 0.1%
-0.9525070193
< 0.1%
-0.94769287111
 
< 0.1%
-0.94731140141
 
< 0.1%
-0.89049530033
< 0.1%
-0.82820129391
 
< 0.1%
ValueCountFrequency (%)
1023.9995121
< 0.1%
1023.8491211
< 0.1%
1023.7370611
< 0.1%
1023.4679571
< 0.1%
1023.2876591
< 0.1%
1023.1345831
< 0.1%
1023.0001221
< 0.1%
1022.9631351
< 0.1%
1022.8248291
< 0.1%
1022.7646481
< 0.1%

command_name
Categorical

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
Move
5413099 
BuildMobile
1305033 
BuildFactory
1218157 
Attack
645324 
Reclaim
577775 
Other values (25)
1489929 

Length

Max length28
Median length4
Mean length6.653150244
Min length4

Characters and Unicode

Total characters70851506
Distinct characters37
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransportUnloadSpecificUnits
2nd rowFormMove
3rd rowStop
4th rowStop
5th rowTransportUnloadUnits

Common Values

ValueCountFrequency (%)
Move5413099
50.8%
BuildMobile1305033
 
12.3%
BuildFactory1218157
 
11.4%
Attack645324
 
6.1%
Reclaim577775
 
5.4%
Guard421514
 
4.0%
AggressiveMove376746
 
3.5%
Patrol253653
 
2.4%
Upgrade158120
 
1.5%
Repair70532
 
0.7%
Other values (20)209364
 
2.0%

Length

2022-07-27T23:19:24.890022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
move5413099
50.8%
buildmobile1305033
 
12.3%
buildfactory1218157
 
11.4%
attack645324
 
6.1%
reclaim577775
 
5.4%
guard421514
 
4.0%
aggressivemove376746
 
3.5%
patrol253653
 
2.4%
upgrade158120
 
1.5%
repair70532
 
0.7%
Other values (20)209364
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o8768166
12.4%
e8752204
12.4%
M7135400
10.1%
v6226308
 
8.8%
i4967748
 
7.0%
l4687487
 
6.6%
a3454627
 
4.9%
d3135999
 
4.4%
u2954994
 
4.2%
t2953815
 
4.2%
Other values (27)17814758
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57169073
80.7%
Uppercase Letter13682433
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o8768166
15.3%
e8752204
15.3%
v6226308
10.9%
i4967748
8.7%
l4687487
8.2%
a3454627
 
6.0%
d3135999
 
5.5%
u2954994
 
5.2%
t2953815
 
5.2%
r2696328
 
4.7%
Other values (11)8571397
15.0%
Uppercase Letter
ValueCountFrequency (%)
M7135400
52.2%
B2523190
 
18.4%
F1261203
 
9.2%
A1032032
 
7.5%
R649537
 
4.7%
G421514
 
3.1%
P253653
 
1.9%
U207275
 
1.5%
S107006
 
0.8%
T44629
 
0.3%
Other values (6)46994
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin70851506
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o8768166
12.4%
e8752204
12.4%
M7135400
10.1%
v6226308
 
8.8%
i4967748
 
7.0%
l4687487
 
6.6%
a3454627
 
4.9%
d3135999
 
4.4%
u2954994
 
4.2%
t2953815
 
4.2%
Other values (27)17814758
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII70851506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o8768166
12.4%
e8752204
12.4%
M7135400
10.1%
v6226308
 
8.8%
i4967748
 
7.0%
l4687487
 
6.6%
a3454627
 
4.9%
d3135999
 
4.4%
u2954994
 
4.2%
t2953815
 
4.2%
Other values (27)17814758
25.1%

faf_player_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1398
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234940.1606
Minimum145
Maximum440191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.2 MiB
2022-07-27T23:19:24.942899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum145
5-th percentile14492
Q182362
median250231
Q3380088
95-th percentile436101
Maximum440191
Range440046
Interquartile range (IQR)297726

Descriptive statistics

Standard deviation153256.8476
Coefficient of variation (CV)0.652322903
Kurtosis-1.517765668
Mean234940.1606
Median Absolute Deviation (MAD)142769
Skewness-0.1468795764
Sum2.501952246 × 1012
Variance2.348766133 × 1010
MonotonicityNot monotonic
2022-07-27T23:19:24.998405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62848220873
 
2.1%
2642160077
 
1.5%
131692149540
 
1.4%
19844136953
 
1.3%
38512127130
 
1.2%
366310122426
 
1.1%
403743121426
 
1.1%
241137107916
 
1.0%
287686107556
 
1.0%
37801797289
 
0.9%
Other values (1388)9298131
87.3%
ValueCountFrequency (%)
145908
 
< 0.1%
25974665
0.7%
43415895
 
0.1%
43535831
0.3%
6067997
 
0.1%
64827131
 
0.3%
6741028
 
< 0.1%
900551
 
< 0.1%
129422152
 
0.2%
12971930
 
< 0.1%
ValueCountFrequency (%)
440191280
 
< 0.1%
440181390
 
< 0.1%
4401283550
 
< 0.1%
440019157
 
< 0.1%
440001125
 
< 0.1%
439974207
 
< 0.1%
439927118
 
< 0.1%
439900381
 
< 0.1%
439899581
 
< 0.1%
43988714627
0.1%

login
Categorical

HIGH CARDINALITY

Distinct1414
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
Chenbro101
 
220873
nemir
 
160077
Yew
 
149540
Electrician
 
136953
Aldarion
 
127130
Other values (1409)
9854744 

Length

Max length16
Median length12
Mean length8.66422579
Min length2

Characters and Unicode

Total characters92268087
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowExselsior
2nd rowStormLantern
3rd rowSaphed
4th rowdemonstreamer666
5th rowZXCCURSED

Common Values

ValueCountFrequency (%)
Chenbro101220873
 
2.1%
nemir160077
 
1.5%
Yew149540
 
1.4%
Electrician136953
 
1.3%
Aldarion127130
 
1.2%
Knockout01122426
 
1.1%
indicator2020121426
 
1.1%
LaissezFairy107916
 
1.0%
demonstreamer666107556
 
1.0%
Pryanichek97289
 
0.9%
Other values (1404)9298131
87.3%

Length

2022-07-27T23:19:25.056584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chenbro101220873
 
2.1%
nemir160077
 
1.5%
yew149540
 
1.4%
electrician136953
 
1.3%
aldarion127130
 
1.2%
knockout01122426
 
1.1%
indicator2020121426
 
1.1%
laissezfairy107916
 
1.0%
demonstreamer666107556
 
1.0%
pryanichek97289
 
0.9%
Other values (1404)9298131
87.3%

Most occurring characters

ValueCountFrequency (%)
a8302164
 
9.0%
e7096729
 
7.7%
r5737422
 
6.2%
o5418538
 
5.9%
n5270612
 
5.7%
i5229546
 
5.7%
t3826103
 
4.1%
l3354663
 
3.6%
s3354229
 
3.6%
m2442303
 
2.6%
Other values (54)42235778
45.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter69738624
75.6%
Uppercase Letter15528918
 
16.8%
Decimal Number5375942
 
5.8%
Connector Punctuation1393922
 
1.5%
Dash Punctuation230681
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8302164
11.9%
e7096729
 
10.2%
r5737422
 
8.2%
o5418538
 
7.8%
n5270612
 
7.6%
i5229546
 
7.5%
t3826103
 
5.5%
l3354663
 
4.8%
s3354229
 
4.8%
m2442303
 
3.5%
Other values (16)19706315
28.3%
Uppercase Letter
ValueCountFrequency (%)
S1340547
 
8.6%
A1071767
 
6.9%
C1044637
 
6.7%
T1005884
 
6.5%
R973127
 
6.3%
D853719
 
5.5%
B786928
 
5.1%
N777877
 
5.0%
P762160
 
4.9%
L721551
 
4.6%
Other values (16)6190721
39.9%
Decimal Number
ValueCountFrequency (%)
11350216
25.1%
01164595
21.7%
2752828
14.0%
6540225
10.0%
3326720
 
6.1%
4303194
 
5.6%
9272822
 
5.1%
7253955
 
4.7%
8237520
 
4.4%
5173867
 
3.2%
Connector Punctuation
ValueCountFrequency (%)
_1393922
100.0%
Dash Punctuation
ValueCountFrequency (%)
-230681
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin85267542
92.4%
Common7000545
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8302164
 
9.7%
e7096729
 
8.3%
r5737422
 
6.7%
o5418538
 
6.4%
n5270612
 
6.2%
i5229546
 
6.1%
t3826103
 
4.5%
l3354663
 
3.9%
s3354229
 
3.9%
m2442303
 
2.9%
Other values (42)35235233
41.3%
Common
ValueCountFrequency (%)
_1393922
19.9%
11350216
19.3%
01164595
16.6%
2752828
10.8%
6540225
 
7.7%
3326720
 
4.7%
4303194
 
4.3%
9272822
 
3.9%
7253955
 
3.6%
8237520
 
3.4%
Other values (2)404548
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII92268087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a8302164
 
9.0%
e7096729
 
7.7%
r5737422
 
6.2%
o5418538
 
5.9%
n5270612
 
5.7%
i5229546
 
5.7%
t3826103
 
4.1%
l3354663
 
3.6%
s3354229
 
3.6%
m2442303
 
2.6%
Other values (54)42235778
45.8%

rating
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2277
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793.7289536
Minimum-698
Maximum2346
Zeros115913
Zeros (%)1.1%
Negative395127
Negative (%)3.7%
Memory size81.2 MiB
2022-07-27T23:19:25.113326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-698
5-th percentile11
Q1426
median738
Q31188
95-th percentile1639
Maximum2346
Range3044
Interquartile range (IQR)762

Descriptive statistics

Standard deviation506.0873452
Coefficient of variation (CV)0.6376072624
Kurtosis-0.411666717
Mean793.7289536
Median Absolute Deviation (MAD)363
Skewness0.2744341983
Sum8452671239
Variance256124.4009
MonotonicityNot monotonic
2022-07-27T23:19:25.168077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115913
 
1.1%
124819366
 
0.2%
147119153
 
0.2%
47718004
 
0.2%
77417199
 
0.2%
125016560
 
0.2%
47415973
 
0.1%
124015780
 
0.1%
98215457
 
0.1%
55615310
 
0.1%
Other values (2267)10380602
97.5%
ValueCountFrequency (%)
-698161
 
< 0.1%
-682368
< 0.1%
-545799
< 0.1%
-535213
 
< 0.1%
-534837
< 0.1%
-52372
 
< 0.1%
-50848
 
< 0.1%
-503271
 
< 0.1%
-49724
 
< 0.1%
-492385
< 0.1%
ValueCountFrequency (%)
2346432
 
< 0.1%
2338566
< 0.1%
2334534
< 0.1%
23281037
< 0.1%
2327564
< 0.1%
2322653
< 0.1%
2320694
< 0.1%
23141271
< 0.1%
2305781
< 0.1%
22891077
< 0.1%

faction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
UEF
4257355 
Cybran
3151404 
Seraphim
1814377 
Aeon
1426181 

Length

Max length8
Median length6
Mean length4.873573488
Min length3

Characters and Unicode

Total characters51900229
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCybran
2nd rowCybran
3rd rowSeraphim
4th rowCybran
5th rowSeraphim

Common Values

ValueCountFrequency (%)
UEF4257355
40.0%
Cybran3151404
29.6%
Seraphim1814377
17.0%
Aeon1426181
 
13.4%

Length

2022-07-27T23:19:25.223674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:19:25.273160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
uef4257355
40.0%
cybran3151404
29.6%
seraphim1814377
17.0%
aeon1426181
 
13.4%

Most occurring characters

ValueCountFrequency (%)
r4965781
9.6%
a4965781
9.6%
n4577585
 
8.8%
E4257355
 
8.2%
U4257355
 
8.2%
F4257355
 
8.2%
e3240558
 
6.2%
b3151404
 
6.1%
y3151404
 
6.1%
C3151404
 
6.1%
Other values (7)11924247
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32736202
63.1%
Uppercase Letter19164027
36.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r4965781
15.2%
a4965781
15.2%
n4577585
14.0%
e3240558
9.9%
b3151404
9.6%
y3151404
9.6%
p1814377
 
5.5%
h1814377
 
5.5%
i1814377
 
5.5%
m1814377
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
E4257355
22.2%
U4257355
22.2%
F4257355
22.2%
C3151404
16.4%
S1814377
9.5%
A1426181
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin51900229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r4965781
9.6%
a4965781
9.6%
n4577585
 
8.8%
E4257355
 
8.2%
U4257355
 
8.2%
F4257355
 
8.2%
e3240558
 
6.2%
b3151404
 
6.1%
y3151404
 
6.1%
C3151404
 
6.1%
Other values (7)11924247
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII51900229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r4965781
9.6%
a4965781
9.6%
n4577585
 
8.8%
E4257355
 
8.2%
U4257355
 
8.2%
F4257355
 
8.2%
e3240558
 
6.2%
b3151404
 
6.1%
y3151404
 
6.1%
C3151404
 
6.1%
Other values (7)11924247
23.0%

map_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
Vya-3 Protectorate
1548995 
Cadmium Green
1432638 
8 - Badlands_v4
1399496 
Niflheim - Final II v2
1343622 
Cobalt Valley
1056064 
Other values (19)
3868502 

Length

Max length35
Median length29
Mean length15.09206299
Min length5

Characters and Unicode

Total characters160720163
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArctic Refuge - FAF version
2nd rowCrossfire Canal - FAF version
3rd rowCrossfire Canal - FAF version
4th rowCrossfire Canal - FAF version
5th rowCrossfire Canal - FAF version

Common Values

ValueCountFrequency (%)
Vya-3 Protectorate1548995
14.5%
Cadmium Green1432638
13.5%
8 - Badlands_v41399496
13.1%
Niflheim - Final II v21343622
12.6%
Cobalt Valley1056064
9.9%
Floralis914347
8.6%
Cloud Kingdom LE907900
8.5%
Pelagial743285
7.0%
Loki - FAF version423028
 
4.0%
Point of Reach v4378868
 
3.6%
Other values (14)501074
 
4.7%

Length

2022-07-27T23:19:25.323761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3336968
 
11.9%
vya-31548995
 
5.5%
protectorate1548995
 
5.5%
green1432638
 
5.1%
cadmium1432638
 
5.1%
81399496
 
5.0%
badlands_v41399496
 
5.0%
niflheim1343622
 
4.8%
final1343622
 
4.8%
ii1343622
 
4.8%
Other values (40)11800612
42.2%

Most occurring characters

ValueCountFrequency (%)
17281387
 
10.8%
a14148705
 
8.8%
l11732982
 
7.3%
e10654749
 
6.6%
i9739124
 
6.1%
o8839220
 
5.5%
r6608347
 
4.1%
t6316215
 
3.9%
n6298314
 
3.9%
d6115548
 
3.8%
Other values (37)62985572
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter107089826
66.6%
Uppercase Letter23960774
 
14.9%
Space Separator17281387
 
10.8%
Decimal Number6070477
 
3.8%
Dash Punctuation4885963
 
3.0%
Connector Punctuation1399496
 
0.9%
Other Punctuation32240
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a14148705
13.2%
l11732982
11.0%
e10654749
9.9%
i9739124
9.1%
o8839220
8.3%
r6608347
 
6.2%
t6316215
 
5.9%
n6298314
 
5.9%
d6115548
 
5.7%
m5275351
 
4.9%
Other values (12)21361271
19.9%
Uppercase Letter
ValueCountFrequency (%)
C3587819
15.0%
F3497780
14.6%
P2751449
11.5%
I2687244
11.2%
V2605059
10.9%
G1463198
6.1%
B1399496
 
5.8%
N1343622
 
5.6%
L1330928
 
5.6%
E980711
 
4.1%
Other values (7)2313468
9.7%
Decimal Number
ValueCountFrequency (%)
41778364
29.3%
31548995
25.5%
81399496
23.1%
21343622
22.1%
Space Separator
ValueCountFrequency (%)
17281387
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4885963
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1399496
100.0%
Other Punctuation
ValueCountFrequency (%)
'32240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin131050600
81.5%
Common29669563
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a14148705
 
10.8%
l11732982
 
9.0%
e10654749
 
8.1%
i9739124
 
7.4%
o8839220
 
6.7%
r6608347
 
5.0%
t6316215
 
4.8%
n6298314
 
4.8%
d6115548
 
4.7%
m5275351
 
4.0%
Other values (29)45322045
34.6%
Common
ValueCountFrequency (%)
17281387
58.2%
-4885963
 
16.5%
41778364
 
6.0%
31548995
 
5.2%
_1399496
 
4.7%
81399496
 
4.7%
21343622
 
4.5%
'32240
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII160720163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17281387
 
10.8%
a14148705
 
8.8%
l11732982
 
7.3%
e10654749
 
6.6%
i9739124
 
6.1%
o8839220
 
5.5%
r6608347
 
4.1%
t6316215
 
3.9%
n6298314
 
3.9%
d6115548
 
3.8%
Other values (37)62985572
39.2%

map_thumbnail
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
https://content.faforever.com/maps/previews/large/scmp_026.png
1548995 
https://content.faforever.com/maps/previews/large/cadmium_green.v0001.png
1432638 
https://content.faforever.com/maps/previews/large/8 - badlands_v4.v0001.png
1399496 
https://content.faforever.com/maps/previews/large/niflheim_-_final_ii_v2.v0002.png
1343622 
https://content.faforever.com/maps/previews/large/cobalt valley v1.png
1056064 
Other values (19)
3868502 

Length

Max length95
Median length87
Mean length72.19683131
Min length62

Characters and Unicode

Total characters768846943
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://content.faforever.com/maps/previews/large/arctic_refuge_-_faf_version.v0002.png
2nd rowhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png
3rd rowhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png
4th rowhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png
5th rowhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png

Common Values

ValueCountFrequency (%)
https://content.faforever.com/maps/previews/large/scmp_026.png1548995
14.5%
https://content.faforever.com/maps/previews/large/cadmium_green.v0001.png1432638
13.5%
https://content.faforever.com/maps/previews/large/8 - badlands_v4.v0001.png1399496
13.1%
https://content.faforever.com/maps/previews/large/niflheim_-_final_ii_v2.v0002.png1343622
12.6%
https://content.faforever.com/maps/previews/large/cobalt valley v1.png1056064
9.9%
https://content.faforever.com/maps/previews/large/floralis.v0003.png914347
8.6%
https://content.faforever.com/maps/previews/large/cloud_kingdom_le.v0017.png907900
8.5%
https://content.faforever.com/maps/previews/large/pelagial.v0004.png743285
7.0%
https://content.faforever.com/maps/previews/large/loki_-_faf_version.v0003.png423028
 
4.0%
https://content.faforever.com/maps/previews/large/point of reach v4.png378868
 
3.6%
Other values (14)501074
 
4.7%

Length

2022-07-27T23:19:25.380864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://content.faforever.com/maps/previews/large/scmp_026.png1548995
 
9.3%
https://content.faforever.com/maps/previews/large/cadmium_green.v0001.png1432638
 
8.6%
https://content.faforever.com/maps/previews/large/81399496
 
8.4%
1399496
 
8.4%
badlands_v4.v0001.png1399496
 
8.4%
https://content.faforever.com/maps/previews/large/niflheim_-_final_ii_v2.v0002.png1343622
 
8.0%
https://content.faforever.com/maps/previews/large/cobalt1056064
 
6.3%
valley1056064
 
6.3%
v1.png1056064
 
6.3%
https://content.faforever.com/maps/previews/large/floralis.v0003.png914347
 
5.5%
Other values (22)4122999
24.6%

Most occurring characters

ValueCountFrequency (%)
e72385717
 
9.4%
/63895902
 
8.3%
r46590364
 
6.1%
p45368017
 
5.9%
t44310404
 
5.8%
a43674052
 
5.7%
n39542232
 
5.1%
.39533446
 
5.1%
o37739301
 
4.9%
s36842359
 
4.8%
Other values (28)298965149
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter590984887
76.9%
Other Punctuation114078665
 
14.8%
Decimal Number40709476
 
5.3%
Connector Punctuation13656983
 
1.8%
Space Separator6079964
 
0.8%
Dash Punctuation3336968
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e72385717
12.2%
r46590364
 
7.9%
p45368017
 
7.7%
t44310404
 
7.5%
a43674052
 
7.4%
n39542232
 
6.7%
o37739301
 
6.4%
s36842359
 
6.2%
v34712093
 
5.9%
m28134240
 
4.8%
Other values (13)161686108
27.4%
Decimal Number
ValueCountFrequency (%)
023492890
57.7%
14980967
 
12.2%
24350016
 
10.7%
42532909
 
6.2%
61548995
 
3.8%
81399496
 
3.4%
31398528
 
3.4%
7955555
 
2.3%
550120
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/63895902
56.0%
.39533446
34.7%
:10649317
 
9.3%
Connector Punctuation
ValueCountFrequency (%)
_13656983
100.0%
Space Separator
ValueCountFrequency (%)
6079964
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3336968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin590984887
76.9%
Common177862056
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e72385717
12.2%
r46590364
 
7.9%
p45368017
 
7.7%
t44310404
 
7.5%
a43674052
 
7.4%
n39542232
 
6.7%
o37739301
 
6.4%
s36842359
 
6.2%
v34712093
 
5.9%
m28134240
 
4.8%
Other values (13)161686108
27.4%
Common
ValueCountFrequency (%)
/63895902
35.9%
.39533446
22.2%
023492890
 
13.2%
_13656983
 
7.7%
:10649317
 
6.0%
6079964
 
3.4%
14980967
 
2.8%
24350016
 
2.4%
-3336968
 
1.9%
42532909
 
1.4%
Other values (5)5352694
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII768846943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e72385717
 
9.4%
/63895902
 
8.3%
r46590364
 
6.1%
p45368017
 
5.9%
t44310404
 
5.8%
a43674052
 
5.7%
n39542232
 
5.1%
.39533446
 
5.1%
o37739301
 
4.9%
s36842359
 
4.8%
Other values (28)298965149
38.9%

map_width
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
512
7489081 
256
1908519 
1024
1251717 

Length

Max length4
Median length3
Mean length3.117539651
Min length3

Characters and Unicode

Total characters33199668
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512
2nd row1024
3rd row1024
4th row1024
5th row1024

Common Values

ValueCountFrequency (%)
5127489081
70.3%
2561908519
 
17.9%
10241251717
 
11.8%

Length

2022-07-27T23:19:25.432060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:19:25.477256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5127489081
70.3%
2561908519
 
17.9%
10241251717
 
11.8%

Most occurring characters

ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33199668
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common33199668
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII33199668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

map_height
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
512
7489081 
256
1908519 
1024
1251717 

Length

Max length4
Median length3
Mean length3.117539651
Min length3

Characters and Unicode

Total characters33199668
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512
2nd row1024
3rd row1024
4th row1024
5th row1024

Common Values

ValueCountFrequency (%)
5127489081
70.3%
2561908519
 
17.9%
10241251717
 
11.8%

Length

2022-07-27T23:19:25.518488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:19:25.565287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5127489081
70.3%
2561908519
 
17.9%
10241251717
 
11.8%

Most occurring characters

ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33199668
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common33199668
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII33199668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210649317
32.1%
59397600
28.3%
18740798
26.3%
61908519
 
5.7%
01251717
 
3.8%
41251717
 
3.8%

Interactions

2022-07-27T23:17:52.726198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:50.373464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:01.586323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:12.328003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:21.713979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:30.856642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:41.745171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:54.368617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:52.077552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:03.113284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:13.800145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:23.042091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:32.167665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:43.380355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:55.993658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:53.720185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:04.749851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:15.104360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:24.330093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:33.460601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:44.992892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:57.406033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:55.181554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:06.095844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:16.297291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:25.586098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:34.775624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:46.357096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:58.742314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:56.544359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:07.412156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:17.475912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:26.888953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:36.025014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:47.746788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:18:00.548961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:58.300940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:09.144325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:18.973603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:28.217447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:37.334288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:49.443375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:18:02.105587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:16:59.970710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:10.763212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:20.396216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:29.541539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:38.640924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:17:51.089423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-27T23:19:25.607509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-27T23:19:25.689792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-27T23:19:25.768962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-27T23:19:25.846060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-27T23:19:25.920839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-27T23:18:09.199642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-27T23:18:31.159267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-27T23:19:05.852939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-27T23:19:14.115372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idstart_timeoffset_msplayertypeunits_numberblueprintposition_xposition_ycommand_namefaf_player_idloginratingfactionmap_namemap_thumbnailmap_widthmap_height
0166305552022-03-25 09:22:13 UTC6545001issue2NaN43.933487126.474571TransportUnloadSpecificUnits82362Exselsior1527CybranArctic Refuge - FAF versionhttps://content.faforever.com/maps/previews/large/arctic_refuge_-_faf_version.v0002.png512512
1165343782022-03-11 20:19:06 UTC9939000issue52NaN310.636597238.775345FormMove156219StormLantern1776CybranCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
2165535442022-03-14 00:27:49 UTC8949000issue1NaNNaNNaNStop304205Saphed1501SeraphimCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
3165535442022-03-14 00:27:49 UTC8144001issue1NaNNaNNaNStop287686demonstreamer6661552CybranCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
4166507212022-03-27 14:52:40 UTC19886000issue1NaN361.272095766.394592TransportUnloadUnits189718ZXCCURSED2216SeraphimCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
5166198242022-03-23 17:52:00 UTC10660000issue1NaN295.902222417.644043TransportUnloadUnits33632silentNoob1657CybranArcanehttps://content.faforever.com/maps/previews/large/arcane.v0001.png512512
6165084572022-03-07 19:16:15 UTC23672000issue1NaN608.132324207.912689TransportUnloadUnits25711Swkoll2107UEFCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
7166182492022-03-23 13:16:43 UTC7670000issue4NaN68.39893397.800713TransportUnloadSpecificUnits19844Electrician1678UEFArcanehttps://content.faforever.com/maps/previews/large/arcane.v0001.png512512
8165084572022-03-07 19:16:15 UTC10680001issue1NaNNaNNaNStop136480Turinturambar2075SeraphimCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
9165292642022-03-11 01:13:06 UTC11861001issue29NaN344.810486301.400574FormMove82362Exselsior1551SeraphimFrithenhttps://content.faforever.com/maps/previews/large/frithen.v0001.png512512

Last rows

idstart_timeoffset_msplayertypeunits_numberblueprintposition_xposition_ycommand_namefaf_player_idloginratingfactionmap_namemap_thumbnailmap_widthmap_height
10649307164680202022-03-01 21:54:51 UTC6896001factory_issue1NaN163.107361196.263565AggressiveMove287686demonstreamer6661570AeonTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649308164680202022-03-01 21:54:51 UTC13333000issue2NaN24.196953147.426407AggressiveMove119070Cast_Away1756CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649309164652852022-03-01 14:09:47 UTC4471001issue3NaN330.260834185.265579AggressiveMove174608MAPTOC1202CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649310164652852022-03-01 14:09:47 UTC7754000issue2NaN329.087463392.115143AggressiveMove304205Saphed1468CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649311164652852022-03-01 14:09:47 UTC3800000issue2NaN125.346039375.933350AggressiveMove304205Saphed1468CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649312164635052022-03-01 03:33:37 UTC7432001issue3NaN284.151703341.428772AggressiveMove219084Conorach1411UEFTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649313164652852022-03-01 14:09:47 UTC7937000issue1NaN217.667480175.959106AggressiveMove304205Saphed1468CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649314164635052022-03-01 03:33:37 UTC4321001issue1NaN354.588531447.169708AggressiveMove219084Conorach1411UEFTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649315164652852022-03-01 14:09:47 UTC6126001issue2NaN418.130646367.067932AggressiveMove174608MAPTOC1202CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512
10649316164652852022-03-01 14:09:47 UTC3970001factory_issue1NaN283.568237312.736023AggressiveMove174608MAPTOC1202CybranTAG Craftious Maximus - FAF versionhttps://content.faforever.com/maps/previews/large/tag_craftious_maximus_-_faf_version.v0004.png512512